Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	129,994
2	South Dakota	125,326
3	Rhode Island	115,974
4	Utah	113,663
5	Arizona	110,343
6	Tennessee	109,560
7	Oklahoma	105,251
8	Iowa	105,069
9	Wisconsin	104,776
10	Arkansas	104,196
11	Nebraska	102,605
12	Kansas	100,216
13	Alabama	98,785
14	Indiana	97,352
15	Mississippi	97,284
16	South Carolina	96,149
17	Idaho	94,447
18	Nevada	94,142
19	Wyoming	92,492
20	Illinois	92,463
21	Montana	91,890
22	Louisiana	90,743
23	Georgia	89,676
24	Texas	89,059
25	California	88,906
26	Kentucky	88,815
27	New Mexico	86,673
28	Florida	86,123
29	Delaware	86,111
30	New Jersey	85,389
31	Minnesota	84,466
32	Missouri	83,342
33	Massachusetts	81,824
34	Ohio	81,048
35	New York	80,647
36	North Carolina	79,909
37	Alaska	77,600
38	Connecticut	76,263
39	Colorado	72,948
40	West Virginia	71,846
41	Pennsylvania	71,163
42	Virginia	65,361
43	Michigan	63,476
44	Maryland	61,886
45	District of Columbia	55,686
46	New Hampshire	53,245
47	Washington	44,037
48	Puerto Rico	41,369
49	Oregon	35,970
50	Maine	32,055
51	Vermont	22,675
52	Hawaii	19,003

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	New Jersey	413
2	South Carolina	390
3	New York	380
4	Alaska	346
5	New Hampshire	338
6	Rhode Island	337
7	Georgia	302
8	North Carolina	296
9	Florida	291
10	Utah	274
11	Pennsylvania	254
12	Virginia	248
13	Idaho	227
14	Wyoming	227
15	Kentucky	225
16	Massachusetts	223
17	Delaware	209
18	Montana	198
19	Arizona	197
20	California	193
21	Alabama	187
22	Iowa	187
23	South Dakota	186
24	Oklahoma	185
25	Colorado	184
26	Washington	176
27	Ohio	174
28	Mississippi	173
29	North Dakota	171
30	Kansas	166
31	West Virginia	162
32	Connecticut	155
33	New Mexico	155
34	Vermont	153
35	Tennessee	149
36	Illinois	147
37	District of Columbia	141
38	Wisconsin	138
39	Missouri	134
40	Nevada	129
41	Indiana	126
42	Minnesota	125
43	Maryland	124
44	Nebraska	124
45	Michigan	122
46	Arkansas	121
47	Texas	121
48	Louisiana	104
49	Maine	102
50	Oregon	98
51	Puerto Rico	69
52	Hawaii	24

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,558
2	New York	2,362
3	Massachusetts	2,275
4	Rhode Island	2,234
5	Mississippi	2,194
6	Connecticut	2,102
7	Arizona	2,101
8	South Dakota	2,087
9	Louisiana	2,023
10	Alabama	1,922
11	North Dakota	1,922
12	Pennsylvania	1,831
13	Indiana	1,824
14	Arkansas	1,763
15	Illinois	1,759
16	New Mexico	1,708
17	Iowa	1,686
18	Michigan	1,626
19	Tennessee	1,605
20	South Carolina	1,583
21	Nevada	1,560
22	Kansas	1,551
23	Georgia	1,498
24	Texas	1,440
25	Ohio	1,421
26	District of Columbia	1,405
27	Florida	1,372
28	Delaware	1,361
29	Missouri	1,317
30	Maryland	1,267
31	Montana	1,251
32	West Virginia	1,247
33	California	1,223
34	Wisconsin	1,171
35	Minnesota	1,147
36	Wyoming	1,143
37	Nebraska	1,111
38	Oklahoma	1,039
39	Colorado	1,035
40	North Carolina	1,030
41	Idaho	1,022
42	Kentucky	1,015
43	New Hampshire	845
44	Virginia	830
45	Washington	639
46	Puerto Rico	607
47	Utah	565
48	Oregon	510
49	Maine	487
50	Alaska	378
51	Vermont	309
52	Hawaii	300

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	14
2	Delaware	14
3	Georgia	14
4	Kansas	13
5	Alabama	12
6	Missouri	11
7	California	10
8	Rhode Island	10
9	Nevada	9
10	New Jersey	9
11	Florida	8
12	Iowa	8
13	Massachusetts	8
14	North Carolina	8
15	Wyoming	8
16	Mississippi	7
17	Pennsylvania	7
18	South Carolina	7
19	New Mexico	6
20	New York	6
21	Ohio	6
22	Arkansas	5
23	District of Columbia	5
24	Kentucky	5
25	Louisiana	5
26	Oklahoma	5
27	Tennessee	5
28	Washington	5
29	Connecticut	4
30	Idaho	4
31	Indiana	4
32	Maryland	4
33	Michigan	4
34	Nebraska	4
35	West Virginia	4
36	Illinois	3
37	New Hampshire	3
38	Texas	3
39	Wisconsin	3
40	Alaska	2
41	Colorado	2
42	Montana	2
43	Puerto Rico	2
44	Virginia	2
45	Maine	1
46	Minnesota	1
47	North Dakota	1
48	Oregon	1
49	South Dakota	1
50	Utah	1
51	Vermont	1
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	319,089	1	99
Chattahoochee	Georgia	275,144	2	99
Bent	Colorado	263,224	3	99
Lincoln	Arkansas	239,942	4	99
Lake	Tennessee	237,885	5	99
Davidson	Tennessee	127,325	222	92
Richland	South Carolina	94,848	1199	61
York	South Carolina	88,701	1504	52
Orange	California	81,299	1845	41
Pierce	Washington	41,400	2899	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Iron	Wisconsin	6,858	3	99
Galax city	Virginia	6,775	4	99
Buffalo	South Dakota	6,626	5	99
Davidson	Tennessee	1,186	1934	38
Orange	California	1,160	1965	37
Richland	South Carolina	1,147	1979	37
York	South Carolina	1,082	2065	34
Pierce	Washington	580	2684	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.